


'''simple docstring'''


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: int       =    0
							A_				: bool       =    False
							A_				: float       =    3.0





class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											self.assertDictEqual(MockClass().to_kwargs()       ,   {}    )
											self.assertDictEqual(MockClass(a=2    ).to_kwargs()       ,   {"""a""": 2}    )
											self.assertDictEqual(MockClass(a=2       ,   b=__snake_case    ).to_kwargs()       ,   {"""a""": 2, """b""": True}    )
											self.assertDictEqual(MockClass(a=2       ,   c=2.25    ).to_kwargs()       ,   {"""a""": 2, """c""": 2.25}    )






							@require_cuda
							def UpperCAmelCase_      (       self    ):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= GradScalerKwargs(init_scale=1024       ,   growth_factor=2    )
											AcceleratorState._reset_state()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Accelerator(mixed_precision="""fp16"""       ,   kwargs_handlers=[scaler_handler]    )
											print(accelerator.use_fpaa    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale       ,   1024.0    )
											self.assertEqual(scaler._growth_factor       ,   2.0    )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor       ,   0.5    )
											self.assertEqual(scaler._growth_interval       ,   2000    )
											self.assertEqual(scaler._enabled       ,   __snake_case    )






							@require_multi_gpu
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
											execute_subprocess_async(__snake_case       ,   env=os.environ.copy()    )


if __name__ == "__main__":
				UpperCAmelCase_						:  Tuple				  =						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				UpperCAmelCase_						:  Union[str, Any]				  =						Accelerator(kwargs_handlers=[ddp_scaler])
				UpperCAmelCase_						:  Dict				  =						torch.nn.Linear(100, 200)
				UpperCAmelCase_						:  Optional[Any]				  =						accelerator.prepare(model)

				# Check the values changed in kwargs
				UpperCAmelCase_						:  str				  =						''
				UpperCAmelCase_						:  Tuple				  =						model.bucket_bytes_cap // (1024 * 1024)
				if observed_bucket_cap_map != 15:
								error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
				if model.find_unused_parameters is not True:
								error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

				# Check the values of the defaults
				if model.dim != 0:
								error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
				if model.broadcast_buffers is not True:
								error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
				if model.gradient_as_bucket_view is not False:
								error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




